Quantile Regression for Competing Risks Data with Missing Cause of Failure.

ABSTRACT: This paper considers generalized linear quantile regression for competing risks data when the failure type may be missing. Two estimation procedures for the regression co-efficients, including an inverse probability weighted complete-case estimator and an augmented inverse probability weighted estimator, are discussed under the assumption that the failure type is missing at random. The proposed estimation procedures utilize supplemental auxiliary variables for predicting the missing failure type and for informing its distribution. The asymptotic properties of the two estimators are derived and their asymptotic efficiencies are compared. We show that the augmented estimator is more efficient and possesses a double robustness property against misspecification of either the model for missingn

SUBMITTER: Sun Y 

PROVIDER: S-EPMC3742132 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature